A real world cityscape may be represented by a point cloud. Point cloud data may comprise a plurality of points, where each point is identified by a coordinate and represents a point of a determined surface location of a point on a building/cityscape. For example, the points may be defined using x, y and z coordinates in a Cartesian coordinate system, where the collection of points represents the surface of the cityscape, such as the surface of roads, sidewalks, buildings, etc. The point cloud may be obtained via measurement by a 3D scanner, such as LiDAR. LiDAR data of a cityscape may be obtained by flying over a location and scanning the ground area with a laser. LiDAR data may be obtained from public sources, such as, e.g., from the United States Interagency Elevation Inventory, the United States National Oceanic Atmospheric Administration (NOAA) and the United States Geological Survey.
Although point cloud data is useful for visually representing a scanned surface to a user, it may be insufficient to use as a computer model for other purposes due to an absence of information at locations between the individual points. Converting the point cloud data to a 3D surface model can address some of these deficiencies, but such a 3D surface model also lacks detailed information that may be desirable for certain applications. Systems and methods to automatically provide further detail to a 3D model of a real world cityscape are desired.